{"ID":5675242,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-05T08:58:33.67720162Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01901","arxiv_id":"2607.01901","title":"SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs","abstract":"Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from large language models (LLMs) as auxiliary features or supervision, limiting their direct role in decision-making and constraining classification stability and robustness. To overcome this, we propose a semantic-aligned brain network framework that actively integrates LLM-derived semantics into the prediction process. Specifically, ROI-level semantics are first incorporated via global self-attention to enrich node representations and provide whole-brain context. Multi-scale hypergraphs are then constructed to explicitly model functional subnetworks and multi-ROI interactions, addressing the locality limitations of traditional GNNs and capturing high-order dependencies. Finally, a decision-level semantic alignment mechanism selectively injects patient-specific textual embeddings into graph representations, enabling semantics to directly guide predictions without perturbing the underlying network structure. Experiments on public brain network datasets ABIDE and ADHD-200 demonstrate state-of-the-art performance, enhanced stability, and improved interpretability, particularly in small-sample settings.","short_abstract":"Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from large language models (LLMs) as auxiliary features or supervision, limiting their direct role in decision-making and constraining classificatio...","url_abs":"https://arxiv.org/abs/2607.01901","url_pdf":"https://arxiv.org/pdf/2607.01901v1","authors":"[\"Yidan Xu\",\"Xiangmin Han\",\"Rundong Xue\",\"Huihui Ye\"]","published":"2026-07-02T08:58:47Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.MM\"]","methods":"[\"Large Language Model\",\"Language Model\",\"Graph Neural Network\"]","has_code":false}